Comparing Dataset A and Dataset B

Dataset statistics

	Dataset A	Dataset B
Number of variables	12	12
Number of observations	446	446
Missing cells	434	442
Missing cells (%)	8.1%	8.3%
Duplicate rows	0	0
Duplicate rows (%)	0.0%	0.0%
Total size in memory	45.3 KiB	45.3 KiB
Average record size in memory	104.0 B	104.0 B

Variable types

	Dataset A	Dataset B
Numeric	5	5
Categorical	4	4
Text	3	3

Alerts

Dataset A	Dataset B
`Sex` is highly overall correlated with `Survived`	Alert not present in this dataset	High correlation
`Survived` is highly overall correlated with `Sex`	Alert not present in this dataset	High correlation
`Age` has 87 (19.5%) missing values	`Age` has 87 (19.5%) missing values	Missing
`Cabin` has 345 (77.4%) missing values	`Cabin` has 354 (79.4%) missing values	Missing
`PassengerId` has unique values	`PassengerId` has unique values	Unique
`Name` has unique values	`Name` has unique values	Unique
`SibSp` has 304 (68.2%) zeros	`SibSp` has 296 (66.4%) zeros	Zeros
`Parch` has 337 (75.6%) zeros	`Parch` has 341 (76.5%) zeros	Zeros
`Fare` has 6 (1.3%) zeros	`Fare` has 9 (2.0%) zeros	Zeros
Alert not present in this dataset	`Fare` is highly overall correlated with `SibSp`	High correlation
Alert not present in this dataset	`SibSp` is highly overall correlated with `Fare`	High correlation

Reproduction

	Dataset A	Dataset B
Analysis started	2024-10-16 08:47:10.498563	2024-10-16 08:47:13.606847
Analysis finished	2024-10-16 08:47:13.603553	2024-10-16 08:47:16.740179
Duration	3.1 seconds	3.13 seconds
Software version	ydata-profiling v0.0.dev0	ydata-profiling v0.0.dev0
Download configuration	config.json	config.json

PassengerId
Real number (ℝ)

	Dataset A	Dataset B
Distinct	446	446
Distinct (%)	100.0%	100.0%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	466.20852	445.96637

	Dataset A	Dataset B
Minimum	1	2
Maximum	891	885
Zeros	0	0
Zeros (%)	0.0%	0.0%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	1	2
5-th percentile	44.25	51.25
Q1	247.75	229.5
median	478	447
Q3	694.5	656.75
95-th percentile	849.25	827.5
Maximum	891	885
Range	890	883
Interquartile range (IQR)	446.75	427.25

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	257.54667	251.05841
Coefficient of variation (CV)	0.55242806	0.56295368
Kurtosis	-1.18671	-1.1765655
Mean	466.20852	445.96637
Median Absolute Deviation (MAD)	222	216
Skewness	-0.11634189	-0.028202166
Sum	207929	198901
Variance	66330.287	63030.325
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
767	1	0.2%
517	1	0.2%
692	1	0.2%
536	1	0.2%
495	1	0.2%
213	1	0.2%
399	1	0.2%
818	1	0.2%
886	1	0.2%
108	1	0.2%
Other values (436)	436	97.8%

Value	Count	Frequency (%)
738	1	0.2%
157	1	0.2%
222	1	0.2%
773	1	0.2%
875	1	0.2%
386	1	0.2%
696	1	0.2%
571	1	0.2%
788	1	0.2%
594	1	0.2%
Other values (436)	436	97.8%

Value	Count	Frequency (%)
1	1	0.2%
2	1	0.2%
6	1	0.2%
8	1	0.2%
9	1	0.2%
10	1	0.2%
11	1	0.2%
12	1	0.2%
14	1	0.2%
15	1	0.2%

Value	Count	Frequency (%)
2	1	0.2%
3	1	0.2%
8	1	0.2%
9	1	0.2%
10	1	0.2%
12	1	0.2%
13	1	0.2%
16	1	0.2%
18	1	0.2%
22	1	0.2%

Value	Count	Frequency (%)
2	1	0.2%
3	1	0.2%
8	1	0.2%
9	1	0.2%
10	1	0.2%
12	1	0.2%
13	1	0.2%
16	1	0.2%
18	1	0.2%
22	1	0.2%

Value	Count	Frequency (%)
1	1	0.2%
2	1	0.2%
6	1	0.2%
8	1	0.2%
9	1	0.2%
10	1	0.2%
11	1	0.2%
12	1	0.2%
14	1	0.2%
15	1	0.2%

Survived
Categorical

	Dataset A	Dataset B
Distinct	2	2
Distinct (%)	0.4%	0.4%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

0	279
1	167

0	282
1	164

Length

	Dataset A	Dataset B
Max length	1	1
Median length	1	1
Mean length	1	1
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	446	446
Distinct characters	2	2
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	1	1
2nd row	1	0
3rd row	1	0
4th row	0	1
5th row	0	0

Common Values

Value	Count	Frequency (%)
0	279	62.6%
1	167	37.4%

Value	Count	Frequency (%)
0	282	63.2%
1	164	36.8%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
0	279	62.6%
1	167	37.4%

Value	Count	Frequency (%)
0	282	63.2%
1	164	36.8%

Most occurring characters

Value	Count	Frequency (%)
0	279	62.6%
1	167	37.4%

Value	Count	Frequency (%)
0	282	63.2%
1	164	36.8%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	446	100.0%

Value	Count	Frequency (%)
(unknown)	446	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
0	279	62.6%
1	167	37.4%

Value	Count	Frequency (%)
0	282	63.2%
1	164	36.8%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	446	100.0%

Value	Count	Frequency (%)
(unknown)	446	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
0	279	62.6%
1	167	37.4%

Value	Count	Frequency (%)
0	282	63.2%
1	164	36.8%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	446	100.0%

Value	Count	Frequency (%)
(unknown)	446	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
0	279	62.6%
1	167	37.4%

Value	Count	Frequency (%)
0	282	63.2%
1	164	36.8%

Pclass
Categorical

	Dataset A	Dataset B
Distinct	3	3
Distinct (%)	0.7%	0.7%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

3	251
1	109
2	86

3	256
1	104
2	86

Length

	Dataset A	Dataset B
Max length	1	1
Median length	1	1
Mean length	1	1
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	446	446
Distinct characters	3	3
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	2	3
2nd row	3	2
3rd row	2	2
4th row	3	2
5th row	3	2

Common Values

Value	Count	Frequency (%)
3	251	56.3%
1	109	24.4%
2	86	19.3%

Value	Count	Frequency (%)
3	256	57.4%
1	104	23.3%
2	86	19.3%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
3	251	56.3%
1	109	24.4%
2	86	19.3%

Value	Count	Frequency (%)
3	256	57.4%
1	104	23.3%
2	86	19.3%

Most occurring characters

Value	Count	Frequency (%)
3	251	56.3%
1	109	24.4%
2	86	19.3%

Value	Count	Frequency (%)
3	256	57.4%
1	104	23.3%
2	86	19.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	446	100.0%

Value	Count	Frequency (%)
(unknown)	446	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
3	251	56.3%
1	109	24.4%
2	86	19.3%

Value	Count	Frequency (%)
3	256	57.4%
1	104	23.3%
2	86	19.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	446	100.0%

Value	Count	Frequency (%)
(unknown)	446	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
3	251	56.3%
1	109	24.4%
2	86	19.3%

Value	Count	Frequency (%)
3	256	57.4%
1	104	23.3%
2	86	19.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	446	100.0%

Value	Count	Frequency (%)
(unknown)	446	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
3	251	56.3%
1	109	24.4%
2	86	19.3%

Value	Count	Frequency (%)
3	256	57.4%
1	104	23.3%
2	86	19.3%

Name
['Text', 'Text']

	Dataset A	Dataset B
Distinct	446	446
Distinct (%)	100.0%	100.0%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Length

	Dataset A	Dataset B
Max length	82	57
Median length	49	48
Mean length	26.975336	27.152466
Min length	12	12

Characters and Unicode

	Dataset A	Dataset B
Total characters	12031	12110
Distinct characters	59	58
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	446	446 ?
Unique (%)	100.0%	100.0%

Sample

	Dataset A	Dataset B
1st row	Lemore, Mrs. (Amelia Milley)	Gilnagh, Miss. Katherine "Katie"
2nd row	Karun, Miss. Manca	Bracken, Mr. James H
3rd row	Hart, Miss. Eva Miriam	Mack, Mrs. (Mary)
4th row	Stanley, Mr. Edward Roland	Abelson, Mrs. Samuel (Hannah Wizosky)
5th row	Perkin, Mr. John Henry	Davies, Mr. Charles Henry

Value	Count	Frequency (%)
mr	261	14.3%
miss	94	5.2%
mrs	60	3.3%
william	36	2.0%
john	23	1.3%
master	20	1.1%
henry	18	1.0%
james	14	0.8%
george	13	0.7%
mary	13	0.7%
Other values (897)	1267	69.7%

Value	Count	Frequency (%)
mr	268	14.7%
miss	83	4.6%
mrs	68	3.7%
william	30	1.6%
master	23	1.3%
henry	22	1.2%
john	20	1.1%
charles	13	0.7%
james	13	0.7%
mary	12	0.7%
Other values (892)	1270	69.7%

Most occurring characters

Value	Count	Frequency (%)
	1375	11.4%
r	963	8.0%
e	839	7.0%
a	796	6.6%
n	671	5.6%
i	663	5.5%
s	651	5.4%
M	553	4.6%
l	536	4.5%
o	518	4.3%
Other values (49)	4466	37.1%

Value	Count	Frequency (%)
	1378	11.4%
r	982	8.1%
a	856	7.1%
e	849	7.0%
n	670	5.5%
i	645	5.3%
s	642	5.3%
M	568	4.7%
l	535	4.4%
o	488	4.0%
Other values (48)	4497	37.1%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	12031	100.0%

Value	Count	Frequency (%)
(unknown)	12110	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	1375	11.4%
r	963	8.0%
e	839	7.0%
a	796	6.6%
n	671	5.6%
i	663	5.5%
s	651	5.4%
M	553	4.6%
l	536	4.5%
o	518	4.3%
Other values (49)	4466	37.1%

Value	Count	Frequency (%)
	1378	11.4%
r	982	8.1%
a	856	7.1%
e	849	7.0%
n	670	5.5%
i	645	5.3%
s	642	5.3%
M	568	4.7%
l	535	4.4%
o	488	4.0%
Other values (48)	4497	37.1%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	12031	100.0%

Value	Count	Frequency (%)
(unknown)	12110	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	1375	11.4%
r	963	8.0%
e	839	7.0%
a	796	6.6%
n	671	5.6%
i	663	5.5%
s	651	5.4%
M	553	4.6%
l	536	4.5%
o	518	4.3%
Other values (49)	4466	37.1%

Value	Count	Frequency (%)
	1378	11.4%
r	982	8.1%
a	856	7.1%
e	849	7.0%
n	670	5.5%
i	645	5.3%
s	642	5.3%
M	568	4.7%
l	535	4.4%
o	488	4.0%
Other values (48)	4497	37.1%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	12031	100.0%

Value	Count	Frequency (%)
(unknown)	12110	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	1375	11.4%
r	963	8.0%
e	839	7.0%
a	796	6.6%
n	671	5.6%
i	663	5.5%
s	651	5.4%
M	553	4.6%
l	536	4.5%
o	518	4.3%
Other values (49)	4466	37.1%

Value	Count	Frequency (%)
	1378	11.4%
r	982	8.1%
a	856	7.1%
e	849	7.0%
n	670	5.5%
i	645	5.3%
s	642	5.3%
M	568	4.7%
l	535	4.4%
o	488	4.0%
Other values (48)	4497	37.1%

Sex
Categorical

	Dataset A	Dataset B
Distinct	2	2
Distinct (%)	0.4%	0.4%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

male	292
female	154

male	294
female	152

Length

	Dataset A	Dataset B
Max length	6	6
Median length	4	4
Mean length	4.690583	4.6816143
Min length	4	4

Characters and Unicode

	Dataset A	Dataset B
Total characters	2092	2088
Distinct characters	5	5
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	female	female
2nd row	female	male
3rd row	female	female
4th row	male	female
5th row	male	male

Common Values

Value	Count	Frequency (%)
male	292	65.5%
female	154	34.5%

Value	Count	Frequency (%)
male	294	65.9%
female	152	34.1%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
male	292	65.5%
female	154	34.5%

Value	Count	Frequency (%)
male	294	65.9%
female	152	34.1%

Most occurring characters

Value	Count	Frequency (%)
e	600	28.7%
m	446	21.3%
a	446	21.3%
l	446	21.3%
f	154	7.4%

Value	Count	Frequency (%)
e	598	28.6%
m	446	21.4%
a	446	21.4%
l	446	21.4%
f	152	7.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	2092	100.0%

Value	Count	Frequency (%)
(unknown)	2088	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	600	28.7%
m	446	21.3%
a	446	21.3%
l	446	21.3%
f	154	7.4%

Value	Count	Frequency (%)
e	598	28.6%
m	446	21.4%
a	446	21.4%
l	446	21.4%
f	152	7.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	2092	100.0%

Value	Count	Frequency (%)
(unknown)	2088	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	600	28.7%
m	446	21.3%
a	446	21.3%
l	446	21.3%
f	154	7.4%

Value	Count	Frequency (%)
e	598	28.6%
m	446	21.4%
a	446	21.4%
l	446	21.4%
f	152	7.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	2092	100.0%

Value	Count	Frequency (%)
(unknown)	2088	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	600	28.7%
m	446	21.3%
a	446	21.3%
l	446	21.3%
f	154	7.4%

Value	Count	Frequency (%)
e	598	28.6%
m	446	21.4%
a	446	21.4%
l	446	21.4%
f	152	7.3%

Age
Real number (ℝ)

	Dataset A	Dataset B
Distinct	72	81
Distinct (%)	20.1%	22.6%
Missing	87	87
Missing (%)	19.5%	19.5%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	29.646936	29.517187

	Dataset A	Dataset B
Minimum	0.42	0.67
Maximum	74	80
Zeros	0	0
Zeros (%)	0.0%	0.0%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0.42	0.67
5-th percentile	3	4
Q1	20	20
median	28	28
Q3	39	38
95-th percentile	56.1	54.1
Maximum	74	80
Range	73.58	79.33
Interquartile range (IQR)	19	18

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	15.164182	14.679815
Coefficient of variation (CV)	0.51149238	0.49733109
Kurtosis	-0.090554845	0.10730441
Mean	29.646936	29.517187
Median Absolute Deviation (MAD)	9	9
Skewness	0.31833731	0.33728083
Sum	10643.25	10596.67
Variance	229.95241	215.49696
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
25	15	3.4%
21	13	2.9%
19	13	2.9%
26	13	2.9%
24	13	2.9%
29	11	2.5%
18	11	2.5%
22	10	2.2%
27	10	2.2%
30	10	2.2%
Other values (62)	240	53.8%
(Missing)	87	19.5%

Value	Count	Frequency (%)
28	17	3.8%
22	14	3.1%
30	13	2.9%
25	13	2.9%
18	13	2.9%
24	13	2.9%
36	12	2.7%
19	11	2.5%
21	11	2.5%
26	11	2.5%
Other values (71)	231	51.8%
(Missing)	87	19.5%

Value	Count	Frequency (%)
0.42	1	0.2%
0.75	1	0.2%
0.83	2	0.4%
0.92	1	0.2%
1	5	1.1%
2	6	1.3%
3	5	1.1%
4	5	1.1%
5	3	0.7%
6	1	0.2%

Value	Count	Frequency (%)
0.67	1	0.2%
0.75	1	0.2%
0.83	1	0.2%
0.92	1	0.2%
1	4	0.9%
2	5	1.1%
3	3	0.7%
4	6	1.3%
5	3	0.7%
6	1	0.2%

Value	Count	Frequency (%)
0.67	1	0.2%
0.75	1	0.2%
0.83	1	0.2%
0.92	1	0.2%
1	4	0.9%
2	5	1.1%
3	3	0.7%
4	6	1.3%
5	3	0.7%
6	1	0.2%

Value	Count	Frequency (%)
0.42	1	0.2%
0.75	1	0.2%
0.83	2	0.4%
0.92	1	0.2%
1	5	1.1%
2	6	1.3%
3	5	1.1%
4	5	1.1%
5	3	0.7%
6	1	0.2%

SibSp
Real number (ℝ)

	Dataset A	Dataset B
Distinct	7	7
Distinct (%)	1.6%	1.6%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	0.52466368	0.55829596

	Dataset A	Dataset B
Minimum	0	0
Maximum	8	8
Zeros	304	296
Zeros (%)	68.2%	66.4%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0	0
5-th percentile	0	0
Q1	0	0
median	0	0
Q3	1	1
95-th percentile	3	3
Maximum	8	8
Range	8	8
Interquartile range (IQR)	1	1

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	1.0864112	1.1117193
Coefficient of variation (CV)	2.0706811	1.9912723
Kurtosis	16.948289	15.214249
Mean	0.52466368	0.55829596
Median Absolute Deviation (MAD)	0	0
Skewness	3.5645831	3.3814231
Sum	234	249
Variance	1.1802892	1.2359198
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
0	304	68.2%
1	103	23.1%
2	15	3.4%
3	10	2.2%
4	8	1.8%
5	3	0.7%
8	3	0.7%

Value	Count	Frequency (%)
0	296	66.4%
1	108	24.2%
2	16	3.6%
4	10	2.2%
3	10	2.2%
5	3	0.7%
8	3	0.7%

Value	Count	Frequency (%)
0	304	68.2%
1	103	23.1%
2	15	3.4%
3	10	2.2%
4	8	1.8%
5	3	0.7%
8	3	0.7%

Value	Count	Frequency (%)
0	296	66.4%
1	108	24.2%
2	16	3.6%
3	10	2.2%
4	10	2.2%
5	3	0.7%
8	3	0.7%

Value	Count	Frequency (%)
0	296	66.4%
1	108	24.2%
2	16	3.6%
3	10	2.2%
4	10	2.2%
5	3	0.7%
8	3	0.7%

Value	Count	Frequency (%)
0	304	68.2%
1	103	23.1%
2	15	3.4%
3	10	2.2%
4	8	1.8%
5	3	0.7%
8	3	0.7%

Parch
Real number (ℝ)

	Dataset A	Dataset B
Distinct	6	7
Distinct (%)	1.3%	1.6%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	0.39461883	0.38789238

	Dataset A	Dataset B
Minimum	0	0
Maximum	5	6
Zeros	337	341
Zeros (%)	75.6%	76.5%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0	0
5-th percentile	0	0
Q1	0	0
median	0	0
Q3	0	0
95-th percentile	2	2
Maximum	5	6
Range	5	6
Interquartile range (IQR)	0	0

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	0.79682951	0.80990306
Coefficient of variation (CV)	2.0192384	2.0879582
Kurtosis	6.6046872	8.9272329
Mean	0.39461883	0.38789238
Median Absolute Deviation (MAD)	0	0
Skewness	2.3561864	2.5974465
Sum	176	173
Variance	0.63493727	0.65594296
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=6)

Value	Count	Frequency (%)
0	337	75.6%
1	55	12.3%
2	47	10.5%
3	3	0.7%
5	2	0.4%
4	2	0.4%

Value	Count	Frequency (%)
0	341	76.5%
1	51	11.4%
2	47	10.5%
3	3	0.7%
4	2	0.4%
6	1	0.2%
5	1	0.2%

Value	Count	Frequency (%)
0	337	75.6%
1	55	12.3%
2	47	10.5%
3	3	0.7%
4	2	0.4%
5	2	0.4%

Value	Count	Frequency (%)
0	341	76.5%
1	51	11.4%
2	47	10.5%
3	3	0.7%
4	2	0.4%
5	1	0.2%
6	1	0.2%

Value	Count	Frequency (%)
0	341	76.5%
1	51	11.4%
2	47	10.5%
3	3	0.7%
4	2	0.4%
5	1	0.2%
6	1	0.2%

Value	Count	Frequency (%)
0	337	75.6%
1	55	12.3%
2	47	10.5%
3	3	0.7%
4	2	0.4%
5	2	0.4%

Ticket
['Text', 'Text']

	Dataset A	Dataset B
Distinct	378	380
Distinct (%)	84.8%	85.2%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Length

	Dataset A	Dataset B
Max length	18	18
Median length	17	17
Mean length	6.7578475	6.7892377
Min length	4	4

Characters and Unicode

	Dataset A	Dataset B
Total characters	3014	3028
Distinct characters	35	32
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	329	331 ?
Unique (%)	73.8%	74.2%

Sample

	Dataset A	Dataset B
1st row	C.A. 34260	35851
2nd row	349256	220367
3rd row	F.C.C. 13529	S.O./P.P. 3
4th row	A/4 45380	P/PP 3381
5th row	A/5 21174	S.O.C. 14879

Value	Count	Frequency (%)
pc	27	4.8%
c.a	12	2.1%
a/5	9	1.6%
ca	7	1.2%
ston/o	7	1.2%
2	7	1.2%
w./c	5	0.9%
soton/o.q	5	0.9%
f.c.c	5	0.9%
4133	4	0.7%
Other values (398)	479	84.5%

Value	Count	Frequency (%)
pc	25	4.4%
a/5	13	2.3%
c.a	11	1.9%
ca	8	1.4%
w./c	6	1.1%
soton/oq	5	0.9%
ston/o	5	0.9%
2	5	0.9%
347082	5	0.9%
ston/o2	4	0.7%
Other values (402)	480	84.7%

Most occurring characters

Value	Count	Frequency (%)
3	383	12.7%
1	349	11.6%
2	279	9.3%
7	250	8.3%
4	224	7.4%
6	210	7.0%
0	209	6.9%
5	193	6.4%
9	166	5.5%
8	144	4.8%
Other values (25)	607	20.1%

Value	Count	Frequency (%)
3	381	12.6%
1	334	11.0%
2	285	9.4%
7	245	8.1%
4	244	8.1%
6	205	6.8%
0	191	6.3%
5	191	6.3%
9	165	5.4%
8	164	5.4%
Other values (22)	623	20.6%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	3014	100.0%

Value	Count	Frequency (%)
(unknown)	3028	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
3	383	12.7%
1	349	11.6%
2	279	9.3%
7	250	8.3%
4	224	7.4%
6	210	7.0%
0	209	6.9%
5	193	6.4%
9	166	5.5%
8	144	4.8%
Other values (25)	607	20.1%

Value	Count	Frequency (%)
3	381	12.6%
1	334	11.0%
2	285	9.4%
7	245	8.1%
4	244	8.1%
6	205	6.8%
0	191	6.3%
5	191	6.3%
9	165	5.4%
8	164	5.4%
Other values (22)	623	20.6%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	3014	100.0%

Value	Count	Frequency (%)
(unknown)	3028	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
3	383	12.7%
1	349	11.6%
2	279	9.3%
7	250	8.3%
4	224	7.4%
6	210	7.0%
0	209	6.9%
5	193	6.4%
9	166	5.5%
8	144	4.8%
Other values (25)	607	20.1%

Value	Count	Frequency (%)
3	381	12.6%
1	334	11.0%
2	285	9.4%
7	245	8.1%
4	244	8.1%
6	205	6.8%
0	191	6.3%
5	191	6.3%
9	165	5.4%
8	164	5.4%
Other values (22)	623	20.6%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	3014	100.0%

Value	Count	Frequency (%)
(unknown)	3028	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
3	383	12.7%
1	349	11.6%
2	279	9.3%
7	250	8.3%
4	224	7.4%
6	210	7.0%
0	209	6.9%
5	193	6.4%
9	166	5.5%
8	144	4.8%
Other values (25)	607	20.1%

Value	Count	Frequency (%)
3	381	12.6%
1	334	11.0%
2	285	9.4%
7	245	8.1%
4	244	8.1%
6	205	6.8%
0	191	6.3%
5	191	6.3%
9	165	5.4%
8	164	5.4%
Other values (22)	623	20.6%

Fare
Real number (ℝ)

	Dataset A	Dataset B
Distinct	178	177
Distinct (%)	39.9%	39.7%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	32.58066	31.42214

	Dataset A	Dataset B
Minimum	0	0
Maximum	512.3292	512.3292
Zeros	6	9
Zeros (%)	1.3%	2.0%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0	0
5-th percentile	7.225	7.225
Q1	7.8958	7.8958
median	14.4542	14.4542
Q3	30.6958	30.5
95-th percentile	120	110.38748
Maximum	512.3292	512.3292
Range	512.3292	512.3292
Interquartile range (IQR)	22.8	22.6042

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	51.945576	49.948189
Coefficient of variation (CV)	1.5943684	1.5895858
Kurtosis	35.564148	41.011683
Mean	32.58066	31.42214
Median Absolute Deviation (MAD)	6.8042	6.7209
Skewness	5.0014039	5.3397644
Sum	14530.974	14014.274
Variance	2698.3429	2494.8216
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
7.75	21	4.7%
7.8958	20	4.5%
13	16	3.6%
8.05	15	3.4%
10.5	15	3.4%
26	13	2.9%
7.775	10	2.2%
26.55	10	2.2%
7.925	9	2.0%
8.6625	8	1.8%
Other values (168)	309	69.3%

Value	Count	Frequency (%)
7.8958	28	6.3%
8.05	24	5.4%
7.75	20	4.5%
13	19	4.3%
10.5	12	2.7%
26.55	11	2.5%
0	9	2.0%
8.6625	8	1.8%
7.925	8	1.8%
7.2292	8	1.8%
Other values (167)	299	67.0%

Value	Count	Frequency (%)
0	6	1.3%
4.0125	1	0.2%
6.4958	1	0.2%
6.75	1	0.2%
6.95	1	0.2%
6.975	1	0.2%
7.05	5	1.1%
7.0542	2	0.4%
7.125	2	0.4%
7.1417	1	0.2%

Value	Count	Frequency (%)
0	9	2.0%
4.0125	1	0.2%
6.2375	1	0.2%
6.4958	1	0.2%
6.95	1	0.2%
7.05	5	1.1%
7.0542	2	0.4%
7.125	2	0.4%
7.225	5	1.1%
7.2292	8	1.8%

Value	Count	Frequency (%)
0	9	2.0%
4.0125	1	0.2%
6.2375	1	0.2%
6.4958	1	0.2%
6.95	1	0.2%
7.05	5	1.1%
7.0542	2	0.4%
7.125	2	0.4%
7.225	5	1.1%
7.2292	8	1.8%

Value	Count	Frequency (%)
0	6	1.3%
4.0125	1	0.2%
6.4958	1	0.2%
6.75	1	0.2%
6.95	1	0.2%
6.975	1	0.2%
7.05	5	1.1%
7.0542	2	0.4%
7.125	2	0.4%
7.1417	1	0.2%

Cabin
['Text', 'Text']

	Dataset A	Dataset B
Distinct	82	79
Distinct (%)	81.2%	85.9%
Missing	345	354
Missing (%)	77.4%	79.4%
Memory size	7.0 KiB	7.0 KiB

Length

	Dataset A	Dataset B
Max length	15	15
Median length	3	3
Mean length	3.6633663	3.5434783
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	370	326
Distinct characters	18	19
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	68	67 ?
Unique (%)	67.3%	72.8%

Sample

	Dataset A	Dataset B
1st row	F33	E77
2nd row	E44	D20
3rd row	D11	D17
4th row	F4	D9
5th row	A14	C125

Value	Count	Frequency (%)
b96	4	3.3%
b98	4	3.3%
c22	3	2.5%
c26	3	2.5%
c23	3	2.5%
c25	3	2.5%
c27	3	2.5%
g6	3	2.5%
b28	2	1.7%
c52	2	1.7%
Other values (81)	90	75.0%

Value	Count	Frequency (%)
c22	3	2.8%
c26	3	2.8%
d17	2	1.9%
d20	2	1.9%
e67	2	1.9%
g6	2	1.9%
d33	2	1.9%
c124	2	1.9%
b49	2	1.9%
b96	2	1.9%
Other values (80)	84	79.2%

Most occurring characters

Value	Count	Frequency (%)
2	47	12.7%
C	39	10.5%
6	29	7.8%
B	29	7.8%
1	28	7.6%
3	21	5.7%
8	20	5.4%
7	20	5.4%
	19	5.1%
4	18	4.9%
Other values (8)	100	27.0%

Value	Count	Frequency (%)
2	37	11.3%
C	34	10.4%
B	30	9.2%
6	25	7.7%
1	24	7.4%
5	19	5.8%
3	19	5.8%
7	18	5.5%
8	17	5.2%
E	17	5.2%
Other values (9)	86	26.4%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	370	100.0%

Value	Count	Frequency (%)
(unknown)	326	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
2	47	12.7%
C	39	10.5%
6	29	7.8%
B	29	7.8%
1	28	7.6%
3	21	5.7%
8	20	5.4%
7	20	5.4%
	19	5.1%
4	18	4.9%
Other values (8)	100	27.0%

Value	Count	Frequency (%)
2	37	11.3%
C	34	10.4%
B	30	9.2%
6	25	7.7%
1	24	7.4%
5	19	5.8%
3	19	5.8%
7	18	5.5%
8	17	5.2%
E	17	5.2%
Other values (9)	86	26.4%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	370	100.0%

Value	Count	Frequency (%)
(unknown)	326	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
2	47	12.7%
C	39	10.5%
6	29	7.8%
B	29	7.8%
1	28	7.6%
3	21	5.7%
8	20	5.4%
7	20	5.4%
	19	5.1%
4	18	4.9%
Other values (8)	100	27.0%

Value	Count	Frequency (%)
2	37	11.3%
C	34	10.4%
B	30	9.2%
6	25	7.7%
1	24	7.4%
5	19	5.8%
3	19	5.8%
7	18	5.5%
8	17	5.2%
E	17	5.2%
Other values (9)	86	26.4%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	370	100.0%

Value	Count	Frequency (%)
(unknown)	326	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
2	47	12.7%
C	39	10.5%
6	29	7.8%
B	29	7.8%
1	28	7.6%
3	21	5.7%
8	20	5.4%
7	20	5.4%
	19	5.1%
4	18	4.9%
Other values (8)	100	27.0%

Value	Count	Frequency (%)
2	37	11.3%
C	34	10.4%
B	30	9.2%
6	25	7.7%
1	24	7.4%
5	19	5.8%
3	19	5.8%
7	18	5.5%
8	17	5.2%
E	17	5.2%
Other values (9)	86	26.4%

Embarked
Categorical

	Dataset A	Dataset B
Distinct	3	3
Distinct (%)	0.7%	0.7%
Missing	2	1
Missing (%)	0.4%	0.2%
Memory size	7.0 KiB	7.0 KiB

S	325
C	74
Q	45

S	334
C	74
Q	37

Length

	Dataset A	Dataset B
Max length	1	1
Median length	1	1
Mean length	1	1
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	444	445
Distinct characters	3	3
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	S	Q
2nd row	C	S
3rd row	S	S
4th row	S	C
5th row	S	S

Common Values

Value	Count	Frequency (%)
S	325	72.9%
C	74	16.6%
Q	45	10.1%
(Missing)	2	0.4%

Value	Count	Frequency (%)
S	334	74.9%
C	74	16.6%
Q	37	8.3%
(Missing)	1	0.2%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
s	325	73.2%
c	74	16.7%
q	45	10.1%

Value	Count	Frequency (%)
s	334	75.1%
c	74	16.6%
q	37	8.3%

Most occurring characters

Value	Count	Frequency (%)
S	325	73.2%
C	74	16.7%
Q	45	10.1%

Value	Count	Frequency (%)
S	334	75.1%
C	74	16.6%
Q	37	8.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	444	100.0%

Value	Count	Frequency (%)
(unknown)	445	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
S	325	73.2%
C	74	16.7%
Q	45	10.1%

Value	Count	Frequency (%)
S	334	75.1%
C	74	16.6%
Q	37	8.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	444	100.0%

Value	Count	Frequency (%)
(unknown)	445	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
S	325	73.2%
C	74	16.7%
Q	45	10.1%

Value	Count	Frequency (%)
S	334	75.1%
C	74	16.6%
Q	37	8.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	444	100.0%

Value	Count	Frequency (%)
(unknown)	445	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
S	325	73.2%
C	74	16.7%
Q	45	10.1%

Value	Count	Frequency (%)
S	334	75.1%
C	74	16.6%
Q	37	8.3%

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

	Age	Embarked	Fare	Parch	PassengerId	Pclass	Sex	SibSp	Survived
Age	1.000	0.000	0.147	-0.309	0.098	0.277	0.089	-0.144	0.233
Embarked	0.000	1.000	0.162	0.085	0.000	0.258	0.039	0.000	0.145
Fare	0.147	0.162	1.000	0.436	0.012	0.480	0.155	0.451	0.283
Parch	-0.309	0.085	0.436	1.000	-0.014	0.000	0.198	0.468	0.165
PassengerId	0.098	0.000	0.012	-0.014	1.000	0.000	0.056	-0.084	0.096
Pclass	0.277	0.258	0.480	0.000	0.000	1.000	0.145	0.124	0.343
Sex	0.089	0.039	0.155	0.198	0.056	0.145	1.000	0.146	0.552
SibSp	-0.144	0.000	0.451	0.468	-0.084	0.124	0.146	1.000	0.130
Survived	0.233	0.145	0.283	0.165	0.096	0.343	0.552	0.130	1.000

Dataset B

	Age	Embarked	Fare	Parch	PassengerId	Pclass	Sex	SibSp	Survived
Age	1.000	0.181	0.120	-0.242	0.072	0.277	0.072	-0.164	0.154
Embarked	0.181	1.000	0.200	0.000	0.000	0.219	0.144	0.073	0.219
Fare	0.120	0.200	1.000	0.442	-0.004	0.465	0.197	0.523	0.311
Parch	-0.242	0.000	0.442	1.000	0.016	0.000	0.312	0.472	0.149
PassengerId	0.072	0.000	-0.004	0.016	1.000	0.000	0.000	-0.047	0.087
Pclass	0.277	0.219	0.465	0.000	0.000	1.000	0.062	0.179	0.357
Sex	0.072	0.144	0.197	0.312	0.000	0.062	1.000	0.294	0.495
SibSp	-0.164	0.073	0.523	0.472	-0.047	0.179	0.294	1.000	0.218
Survived	0.154	0.219	0.311	0.149	0.087	0.357	0.495	0.218	1.000

Dataset A

A simple visualization of nullity by column.

Dataset B

A simple visualization of nullity by column.

Dataset A

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Dataset B

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Dataset A

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Dataset B

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Dataset A

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
516	517	1	2	Lemore, Mrs. (Amelia Milley)	female	34.0	0	0	C.A. 34260	10.5000	F33	S
691	692	1	3	Karun, Miss. Manca	female	4.0	0	1	349256	13.4167	NaN	C
535	536	1	2	Hart, Miss. Eva Miriam	female	7.0	0	2	F.C.C. 13529	26.2500	NaN	S
494	495	0	3	Stanley, Mr. Edward Roland	male	21.0	0	0	A/4 45380	8.0500	NaN	S
212	213	0	3	Perkin, Mr. John Henry	male	22.0	0	0	A/5 21174	7.2500	NaN	S
398	399	0	2	Pain, Dr. Alfred	male	23.0	0	0	244278	10.5000	NaN	S
817	818	0	2	Mallet, Mr. Albert	male	31.0	1	1	S.C./PARIS 2079	37.0042	NaN	C
885	886	0	3	Rice, Mrs. William (Margaret Norton)	female	39.0	0	5	382652	29.1250	NaN	Q
107	108	1	3	Moss, Mr. Albert Johan	male	NaN	0	0	312991	7.7750	NaN	S
576	577	1	2	Garside, Miss. Ethel	female	34.0	0	0	243880	13.0000	NaN	S

Dataset B

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
156	157	1	3	Gilnagh, Miss. Katherine "Katie"	female	16.0	0	0	35851	7.7333	NaN	Q
221	222	0	2	Bracken, Mr. James H	male	27.0	0	0	220367	13.0000	NaN	S
772	773	0	2	Mack, Mrs. (Mary)	female	57.0	0	0	S.O./P.P. 3	10.5000	E77	S
874	875	1	2	Abelson, Mrs. Samuel (Hannah Wizosky)	female	28.0	1	0	P/PP 3381	24.0000	NaN	C
385	386	0	2	Davies, Mr. Charles Henry	male	18.0	0	0	S.O.C. 14879	73.5000	NaN	S
695	696	0	2	Chapman, Mr. Charles Henry	male	52.0	0	0	248731	13.5000	NaN	S
570	571	1	2	Harris, Mr. George	male	62.0	0	0	S.W./PP 752	10.5000	NaN	S
787	788	0	3	Rice, Master. George Hugh	male	8.0	4	1	382652	29.1250	NaN	Q
593	594	0	3	Bourke, Miss. Mary	female	NaN	0	2	364848	7.7500	NaN	Q
157	158	0	3	Corn, Mr. Harry	male	30.0	0	0	SOTON/OQ 392090	8.0500	NaN	S

Dataset A

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
887	888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.0000	B42	S
373	374	0	1	Ringhini, Mr. Sante	male	22.0	0	0	PC 17760	135.6333	NaN	C
172	173	1	3	Johnson, Miss. Eleanor Ileen	female	1.0	1	1	347742	11.1333	NaN	S
308	309	0	2	Abelson, Mr. Samuel	male	30.0	1	0	P/PP 3381	24.0000	NaN	C
627	628	1	1	Longley, Miss. Gretchen Fiske	female	21.0	0	0	13502	77.9583	D9	S
502	503	0	3	O'Sullivan, Miss. Bridget Mary	female	NaN	0	0	330909	7.6292	NaN	Q
448	449	1	3	Baclini, Miss. Marie Catherine	female	5.0	2	1	2666	19.2583	NaN	C
468	469	0	3	Scanlan, Mr. James	male	NaN	0	0	36209	7.7250	NaN	Q
617	618	0	3	Lobb, Mrs. William Arthur (Cordelia K Stanlick)	female	26.0	1	0	A/5. 3336	16.1000	NaN	S
766	767	0	1	Brewe, Dr. Arthur Jackson	male	NaN	0	0	112379	39.6000	NaN	C

Dataset B

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
71	72	0	3	Goodwin, Miss. Lillian Amy	female	16.0	5	2	CA 2144	46.9000	NaN	S
279	280	1	3	Abbott, Mrs. Stanton (Rosa Hunt)	female	35.0	1	1	C.A. 2673	20.2500	NaN	S
476	477	0	2	Renouf, Mr. Peter Henry	male	34.0	1	0	31027	21.0000	NaN	S
494	495	0	3	Stanley, Mr. Edward Roland	male	21.0	0	0	A/4 45380	8.0500	NaN	S
633	634	0	1	Parr, Mr. William Henry Marsh	male	NaN	0	0	112052	0.0000	NaN	S
617	618	0	3	Lobb, Mrs. William Arthur (Cordelia K Stanlick)	female	26.0	1	0	A/5. 3336	16.1000	NaN	S
226	227	1	2	Mellors, Mr. William John	male	19.0	0	0	SW/PP 751	10.5000	NaN	S
296	297	0	3	Hanna, Mr. Mansour	male	23.5	0	0	2693	7.2292	NaN	C
876	877	0	3	Gustafsson, Mr. Alfred Ossian	male	20.0	0	0	7534	9.8458	NaN	S
737	738	1	1	Lesurer, Mr. Gustave J	male	35.0	0	0	PC 17755	512.3292	B101	C

Dataset A

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	# duplicates
Dataset does not contain duplicate rows.

Dataset B

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	# duplicates
Dataset does not contain duplicate rows.

Overview

Variables

Common Values

Length

Common Values (Plot)

Dataset A

Dataset B

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Common Values

Length

Common Values (Plot)

Dataset A

Dataset B

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Common Values

Length

Common Values (Plot)

Dataset A

Dataset B

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Common Values

Length

Common Values (Plot)